1. Field of the Invention
The subject matter of the present invention generally involves spectral image analysis. More particularly, the present invention involves processes for determining representative scene components, or endmembers, from spectral imagery.
2. Description of the Related Art
It has long been recognized that at spatial scales typical of remotely sensed data, the surface corresponding to a given pixel is rarely composed of a single physical component, but rather a combination of “sub-pixel” constituents. This mixed-pixel concept was developed into a quantitative model for interpretation of spectral images, Spectral Mixture Analysis (“SMA”). SMA involves an interactive selection and refinement of key components, or endmembers, to achieve an acceptable solution. The SMA model is based on the assumption that remotely sensed spectral measurements are mixed signatures, which vary across the scene as the relative proportion of each component changes. SMA can therefore be used to translate spectral imagery into quantitative maps also referred to as abundance maps, showing the percent cover of each spectrally distinct scene component. The number of endmembers resolvable with SMA is dependent on the complexity of the actual surface, the spatial resolution of the imagery, and the spectral resolution of the data. However, even in hyperspectral imagery of complex regions, SMA is most robust when a few (<10) spectrally distinct components are modeled. For instance, a forested scene would be modeled as a combination of green vegetation (e.g. leaves, grass), non-photosynthetic vegetation (NPV, e.g. bark, wood, dry leaves), soils, and shade. This last component, shade, is unique to SMA. The shade endmember accounts for spectral variations in illumination caused by either topography or sub-pixel surface texture. With SMA, a tree or group of trees can therefore be modeled not by a single “tree” signature, but instead by a combination of green vegetation, NPV, and shade. The selection of these types of endmembers generally requires at least one expert user's input to be successful.
Alternatively, complex statistical and mathematical approaches have been employed in order to perform endmember selection, but none have been shown to provide routine and reliable results. These approaches utilize purely mathematical transformations of the spectral data, without regard for what physical information the data actually represents, e.g., vegetation, NPV, soil, shade, buildings, roads, etc. The endmembers determined through these processes are physically meaningless and not comparable to those derived by expert users. As such, their utility is limited and at best require subsequent subjective interpretation by an expert.
Current endmember selection methods are thus either manual with expert users doing the processing or automatic using signal processing, but with less reliable results that still require expert user interpretation.